Overall structure of uncertainty analysis

This section provides a brief overview of the overall structure of uncertainty analysis, as illustrated in Figure 3.1. Emissions/removals estimates are based on: (1) conceptualisation; (2) models; and (3) input data and assumptions (e.g., emission factor and activity data). Each of these three can be a source of uncertainty. The analysis begins with a conceptualisation. This is a set of assumptions regarding the structure of an inventory or of a sector. These assumptions typically include the scope of geographic area, temporal averaging time, categories, emissions or removal processes, and gases that are included. The assumptions and the methodological choice determine the needs for data and information. There can be some interaction between data and assumptions and methodological choice, indicated by the two-way arrow in the figure. For example, the ability to disaggregate categories, which may be necessary for higher tier methodologies, can depend on the availability of data. Data, whether empirical or based on expert judgment, should undergo appropriate data collection and QC procedures, as detailed in Chapters 2, Approaches to Data Collection, and Chapter 6, Quality Assurance/Quality Control and Verification, respectively.

Models can be as simple as arithmetic multiplication of activity and emission factors for each category and subsequent summation over all categories, but they may also include complex process models specific to particular categories. The data and information obtained from data collection become input to a more specific knowledge base of data and judgment for uncertainty, as shown in the figure and as discussed in detail in Section

3.2.1, Sources of Data and Information. Specific causes of uncertainty in the conceptualisation, models, and data are discussed in Section 3.2.1 and techniques for quantifying uncertainties in input data are set out in Section

3.2.2. These necessary data include percentage uncertainty estimates and underlining probability density functions (PDFs - discussed in Section 3.1.4) for input to an emission inventory uncertainty analysis. Methods for combining input uncertainties to arrive at uncertainty estimates for single categories and the overall inventory result are detailed in Section 3.2.3. Two Approaches are given for combining uncertainties. Approach 1 is a relatively simple spreadsheet-based calculation procedure based upon some assumptions to simplify the calculations. Approach 2 is based upon Monte Carlo simulation and can be applied more generally. Either approach provides an estimate of the overall uncertainties associated with the total greenhouse gas inventory.

Figure 3.1 Overall structure of a generic uncertainty analysis

Figure 3.1 Overall structure of a generic uncertainty analysis

Spinal Cord Injury Pathophysiology
Uncertainty Estimates

Note: Shaded Boxes are the focus of this Chapter.

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